Initial Questions to Explore

NOTICE that I am only looking at the general rifle hunting seasons on public land. There are also hunters for Archery, Muzzleloader, Private Land, Ranching for Wildlife, etc. *** ## Setup

setwd("~/_code/colorado-dow/Phase I - Descriptive Analytics")

Load required libraries for wrangling data, charting, and mapping

library(plyr,quietly = T) # data wrangling
library(dplyr,quietly = T) # data wrangling
library(ggplot2, quietly = T) # charting
library(ggthemes,quietly = T) # so I can add the highcharts theme and palette
library(scales,quietly = T) # to load the percent function when labeling plots

Set our preferred charting theme

theme_set(theme_minimal()+theme_hc()+theme(legend.key.width = unit(1.5, "cm")))

Run script to get success data

source('~/_code/colorado-dow/datasets/Colorado Elk Harvest Data.R', echo=F)

Table of the harvest success data

COElkRifleAll
source('~/_code/colorado-dow/datasets/Colorado GMUnit and Road data.R', echo=F)
## OGR data source with driver: ESRI Shapefile 
## Source: "/Users/psarnow/_code/colorado-dow/datasets/CPW_GMUBoundaries/BigGameGMUBoundaries03172015.shp", layer: "BigGameGMUBoundaries03172015"
## with 185 features
## It has 12 fields
## Integer64 fields read as strings:  GMUID 
## OGR data source with driver: ESRI Shapefile 
## Source: "/Users/psarnow/_code/colorado-dow/datasets/ne_10m_roads/ne_10m_roads.shp", layer: "ne_10m_roads"
## with 56601 features
## It has 29 fields
## Integer64 fields read as strings:  scalerank question

Take a peak at the boundary data

head(Unitboundaries2)

Hunter Success

Statewide

Success By Year

# Recalc success using all the hunter and harvest results
COElkSuccessStatewide <- summarise(group_by(COElkRifleAll,Year,Unit),
                                   Hunters = sum(c(Hunters.Antlered,Hunters.Antlerless,Hunters.Either),na.rm = T),
                                   Harvest = sum(c(Harvest.Antlered,Harvest.Antlerless),na.rm = T),
                                   Success = Harvest / Hunters)
# Group Units
COElkSuccessStatewide <- summarise(group_by(COElkSuccessStatewide,Year),
                                   Success = mean(Success))

ggplot(COElkSuccessStatewide, aes(Year,Success)) +
  geom_bar(stat="identity",fill=ggthemes_data$hc$palettes$default[2]) +
  scale_y_continuous(labels = percent) +
  coord_cartesian(ylim = c(.16,.25)) +
  labs(title="Statewide Elk Hunter Success", caption="source: cpw.state.co.us")

There is a general trend since 2006 of declining hunter success. 2009 Did have a ‘reset’ of success and with the exception of 2015 rates have been decreasing. I should note that 2017 was higher than 2016, hopefully that positive trend continues for 2018.

FUTURE what happened in 2009 and 2015 that increased the success rates from their preceeding years?

FUTURE why are success rates declining overall?

Success by Season

COElkSuccessStatewide.Season <- summarise(group_by(COElkRifleAll,Season,Unit),
                                          Hunters = sum(c(Hunters.Antlered,Hunters.Antlerless,Hunters.Either),na.rm = T),
                                          Harvest = sum(c(Harvest.Antlered,Harvest.Antlerless),na.rm = T),
                                          Success = Harvest / Hunters)
# Group Units
COElkSuccessStatewide.Season <- summarise(group_by(COElkSuccessStatewide.Season,Season),
                                   Success = mean(Success))

ggplot(COElkSuccessStatewide.Season, aes(Season,Success)) +
  geom_bar(stat="identity",fill=ggthemes_data$hc$palettes$default[2]) +
  scale_y_continuous(labels = percent) +
  coord_cartesian(ylim = c(.1,.25)) +
  labs(title="Statewide Elk Hunter Success by Season", caption="source: cpw.state.co.us")

First and Fourth seasons have better success rates than the others.

Hunter Success by Unit

# Recalc success using all the hunter and harvest results
COElkSuccess <- summarise(group_by(COElkRifleAll,Year,Unit),
                          Hunters = sum(c(Hunters.Antlered,Hunters.Antlerless,Hunters.Either),na.rm = T),
                          Harvest = sum(c(Harvest.Antlered,Harvest.Antlerless),na.rm = T),
                          Success = Harvest / Hunters)

Last year’s data

Year2017 <- filter(COElkSuccess, Year == "2017")
HunterSuccesstoPlot <- left_join(Unitboundaries2,Year2017, by=c("Unit"))
ggplot(HunterSuccesstoPlot, aes(long, lat, group = group)) + 
  geom_polygon(aes(fill = Success),colour = "grey50", size = .2) + #Unit boundaries
  geom_path(data = COroads,aes(x = long, y = lat, group = group), color="#3878C7",size=2) + #Roads
  geom_text(data=data_centroids,aes(x=longitude,y=latitude,label = Unit),size=3) + #Unit labels
  scale_fill_distiller(palette = "RdPu",direction = 1,na.value = 'light grey') +
  xlab("") + ylab("") +
  labs(title="2017 Colorado Elk Hunter Success", caption="source: cpw.state.co.us")

TODO - commentary

Rank Units by overall hunter success

Would also be beneficial to rank each unit so I can reference later. I’ll average the last few years

HunterSuccessRanklast3 <- filter(COElkSuccess, as.numeric(Year) >= 2015)
HunterSuccessRanklast3 <- summarise(group_by(HunterSuccessRanklast3,Unit),
                                    Success = mean(Success,na.rm = T))
HunterSuccessRanklast3$SuccessRank = rank(-HunterSuccessRanklast3$Success)

HunterSuccessRanklast3 <- filter(HunterSuccessRanklast3, SuccessRank <= 50) # top 50 units

In order for the chart to retain the order of the rows, the X axis variable (i.e. the categories) has to be converted into a factor.

HunterSuccessRanklast3 <- HunterSuccessRanklast3[order(-HunterSuccessRanklast3$Success), ]  # sort
HunterSuccessRanklast3$Unit <- factor(HunterSuccessRanklast3$Unit, levels = HunterSuccessRanklast3$Unit)  # to retain the order in plot.

Lollipop Chart

ggplot(HunterSuccessRanklast3, aes(x=Unit, y=Success)) + 
  geom_point(size=3) + 
  geom_segment(aes(x=Unit, 
                   xend=Unit, 
                   y=0, 
                   yend=Success)) +
  scale_y_continuous(labels = percent) +
  labs(title="Average Hunter Success 2015-2017\nTop 50 Units", subtitle="Success by Unit", caption="source: cpw.state.co.us")

There are a handful of units that have much better success than the rest of the state.

Rank Units with Seasons by overall hunter success

HunterSuccessSeasonRanklast3 <- filter(COElkRifleAll, as.numeric(Year) >= 2015)
HunterSuccessSeasonRanklast3$Season[HunterSuccessSeasonRanklast3$Season == "1"] <- "1st"
HunterSuccessSeasonRanklast3$Season[HunterSuccessSeasonRanklast3$Season == "2"] <- "2nd"
HunterSuccessSeasonRanklast3$Season[HunterSuccessSeasonRanklast3$Season == "3"] <- "3rd"
HunterSuccessSeasonRanklast3$Season[HunterSuccessSeasonRanklast3$Season == "4"] <- "4th"

HunterSuccessSeasonRanklast3$Unit_Season <- paste(HunterSuccessSeasonRanklast3$Unit,HunterSuccessSeasonRanklast3$Season,sep="\n")
HunterSuccessSeasonRanklast3 <- summarise(group_by(HunterSuccessSeasonRanklast3,Unit_Season),
                                          Hunters = sum(c(Hunters.Antlered,Hunters.Antlerless,Hunters.Either),na.rm = T),
                                          Harvest = sum(c(Harvest.Antlered,Harvest.Antlerless),na.rm = T),
                                          Success = Harvest / Hunters)

HunterSuccessSeasonRanklast3$SuccessRank = rank(-HunterSuccessSeasonRanklast3$Success)

HunterSuccessSeasonRanklast3 <- filter(HunterSuccessSeasonRanklast3, SuccessRank <= 50) # top 50 unit seasons

In order for the chart to retain the order of the rows, the X axis variable (i.e. the categories) has to be converted into a factor.

HunterSuccessSeasonRanklast3 <- HunterSuccessSeasonRanklast3[order(-HunterSuccessSeasonRanklast3$Success), ]  # sort
HunterSuccessSeasonRanklast3$Unit_Season <- factor(HunterSuccessSeasonRanklast3$Unit_Season, levels = HunterSuccessSeasonRanklast3$Unit_Season)  # to retain the order in plot.

Lollipop Chart

ggplot(HunterSuccessSeasonRanklast3, aes(x=Unit_Season, y=Success)) + 
  geom_point(size=3) + 
  geom_segment(aes(x=Unit_Season, 
                   xend=Unit_Season, 
                   y=0, 
                   yend=Success)) +
  scale_y_continuous(labels = percent) +
  labs(title="Average Hunter Success 2015-2017\nTop 50 Unit Seasons", subtitle="Success by Unit and Season", caption="source: cpw.state.co.us")

TODO - commentary

Conclusion

First and Fourth seasons have the best success overall. There are a handful of units that have much better recent success rates than the rest of the state, and for the top few it doesn’t even seem season dependent.

I’d like to look at what it takes to get a license to hunt in those units. I can do that by reviewing past draw results.

Look at draw results for all seasons of units 2, 40, 61, 9, 201, 20, 10. What is the likelihood of acquiring a license for a Bull and Cow in each of their seasons?

Is there a unit, season, or sex that has a better likelihood of license success?